1. Good Pharmacovigilance Practices
Min Chen, RPh
Associate Director,
Division of Drug Risk Evaluation
Office of Drug Safety, CDER

2. What is Pharmacovigilance?
Is generally regarded as all postapproval scientific and data gathering activities relating to the detection, assessment, understanding, and prevention of adverse events or any other product-related problems
This includes the use of pharmacoepidemiologic studies

3. Good Pharmacovigilance Practice starts by acquiring complete data from spontaneous adverse event reports

4. Characteristics of a Good Case Report
Adverse event(s) details
Baseline patient characteristics
Therapy details
Time to onset of signs or symptoms
Diagnosis of the event(s)
Clinical course of the event and outcomes
Laboratory data
Any other relevant information

5. Good Medication Error Report
Product(s) involved
Sequence of events leading to the error
Work environment in which the error occurred
Types of personnel involved with the error
Narratives- follow NCC MERP taxonomy (National Coordinating Council for Medication Error Reporting and Prevention)

6. Case Series Development
Includes spontaneous case reports and scientific literature case reports
Good database search strategies using MedDRA terminology
Case definitions may be developed to provide consistent characterizations of the adverse events, and to facilitate retrieval of all clinically relevant cases

7. Assessing Causality
It is rarely certain whether an event is product induced
Features supportive of an association
event occurred in the expected timeframe
absence of symptoms prior to exposure
absence of co-morbid conditions or use of concomitant medications
(+) dechallenge, (+) rechallenge
event consistent with the established mechanism of action of product
Grouping case reports into “probable,” “possible,” and “unlikely” when appropriate

8. Safety Signals
An apparent excess of adverse events associated with a product’s use
a single well-documented case report may be viewed as a signal
preclinical findings
experience with other similar products in the class
May be further assessed in terms of magnitude, population at risk, changes in risk over time, biological plausibility and other factors

9. Data Mining As a Signaling Tool
Uses raw case report data and arrays of all drug-adverse events combinations
Calculates “expected” (E) number of events for all drugs, then compares each drug’s “observed” (O) to “expected”
Various methods may be used to generate relative signal scores (intensity) of O:E
May be useful adjunct to routine safety signaling methods
Further exploration and validation is needed

10. Safety Signals May Be... New unlabeled adverse events
An observed increase in the severity or specificity of a labeled event
An observed increase in the frequency of a labeled event
New interactions
Confusion with a product’s name, packaging or use, either actual or potential

11. Understanding Safety Signals
Demographics - age, gender, race
Effect of exposure duration and dose
Relationship between concomitant medications and potential interactions and the risk of event
Relationship between co-morbid conditions and the risk of event

12. Understanding Safety Signals (cont’d)
Effects of lot-to-lot variation and differences in product formulation and the risk of the event
Potential for an excess of adverse events given the disease being treated
Estimates of the magnitude of risk or differences from known background rates

13. Questions for Public Comment
How can the quality of spontaneously reported case reports be improved?

14. Questions for Public Comment
What are possible advantages or disadvantages of applying data mining techniques (e.g., empirical Bayesian techniques, proportional reporting ratios) to spontaneous reports databases for the purpose of identifying safety signals?

15. Questions for Public Comment
What are possible advantages or disadvantages of performing causality assessments at the individual case level?